On Incentivized Exploration beyond Bayesianism and Full-Information

Abstract:We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al. [2014]. We consider agents that may possess external information unknown to the principal. We show such settings require new notions of incentivized exploration, as well as going beyond a Bayesian perspective, and we introduce a definition where agents choose any reasonable (undominated) action. Furthermore, our framework provides for a more robust treatment of ties, and extends to settings where agents lack a single common prior and instead only know that reward distributions belong to a collection of potential priors.

Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)

arXiv:2607.18300 [cs.GT]

(or arXiv:2607.18300v1 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2607.18300 Focus to learn more arXiv-issued DOI via DataCite

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